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YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos

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Advanced Network Technologies and Intelligent Computing (ANTIC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1798))

Abstract

Now-a-days, Object detection algorithms becomes more popular because of their significant contribution to the field of computer vision. Object detection algorithms are divided into two approaches i) region-based approach and ii) region-free approach. In this paper, we implemented YOLOv4 and YOLOv5 techniques of region-free approach due to their high detection speed and accuracy. The objective of this paper is to identify the relevant and non-relevant parts of surveillance videos as watching entire video footage is a time-consuming process. The use case for this research work is ATM surveillance footage, where the dataset is publicly not available, to train the network we developed our data set and then train and compare the proposed models. After experimental results, it is observed that YOLOv5 archives 84% accuracy and give better results than YOLOv4 which achieve 56% accuracy only.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    Nikita Mohod, Prateek Agrawal & Vishu Madaan

  2. SIPNA College of Engineering and Technology, Amravati, Maharashtra, India

    Nikita Mohod

Authors
  1. Nikita Mohod
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  2. Prateek Agrawal
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  3. Vishu Madaan
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Corresponding author

Correspondence to Nikita Mohod .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  2. Netaji Subhas University of Technology, New Delhi, India

    Sanjay Kumar Dhurandher

  3. ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Kiran Kumar Pattanaik

  4. Banaras Hindu University, Varanasi, India

    Anshul Verma

  5. Indian Institute of Technology, Patna, India

    Pradeepika Verma

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Mohod, N., Agrawal, P., Madaan, V. (2023). YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham. https://doi.org/10.1007/978-3-031-28183-9_46

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  • DOI: https://doi.org/10.1007/978-3-031-28183-9_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28182-2

  • Online ISBN: 978-3-031-28183-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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